There are many situations where the courses of multiple objects in a region must be tracked. Typically, radar is used to scan the region and generate discrete images or “snapshots” based on sets of returns. In some types of tracking systems, all the returns from any one object are represented in an image as a single point unrelated to the shape or size of the objects. “Tracking” is the process of identifying a sequence of points from a respective sequence of the images that represents the motion of an object. The tracking problem is difficult when there are multiple closely spaced objects because the objects can change their speed and direction rapidly and move into and out of the line of sight for other objects. The problem is exacerbated because each set of returns may result from noise as well as echoes from the actual objects. The returns resulting from the noise are also called false positives. Likewise, the radar will not detect all echoes from the actual objects and this phenomena is called a false negative or ‘missed detect’ error. For tracking airborne objects, a large distance between the radar and the objects diminishes the signal to noise ratio so the number of false positives and false negatives can be high. For robotic applications the power of the radar is low and as a result, the signal to noise ratio can also be low and the number of false positives and false negatives high.
Accordingly there exists a need to provide an improved radar system for detecting, identifying, and tracking objects and dismounts over a wide area, even in noisy conditions.